文件名称：Statistical Application Development with R and Python, 2nd-Packt (2017).pdf
文件大小：7.58MB
文件格式：PDF
更新时间：2021-04-18 02:16:49
Python
R and Python are interchangeably required languages these days for anybody engaged with data analysis. The growth of these two languages and their inter-dependency creates a natural requirement to learn them both. Thus, it was natural where the second edition of my previous title R Statistical Application Development by Example was headed. I thus took this opportunity to add Python as an important layer and hence you would nd Doing it in Python spread across and throughout the book. Now, the book is useful on many fronts, those who need to learn both the languages, uses R and needs to switch to Python, and vice versa. While abstract development of ideas and algorithms have been retained in R only, standard and more commonly required data analysis technique are available in both the languages now. The only reason for not providing the Python parallel is to avoid the book from becoming too bulky. The open source language R is fast becoming one of the preferred companions for statistics, even as the subject continues to add many friends in machine learning, data mining, and so on among its already rich scienti c network. The era of mathematical theory and statistical application embeddedness is truly a remarkable one for society and R and Python has played a very pivotal role in it. This book is a humble attempt at presenting statistical models through R for any reader who has a bit of familiarity with the subject. In my experience of practicing the subject with colleagues and friends from different backgrounds, I realized that many are interested in learning the subject and applying it in their domain which enables them to take appropriate decisions in analyses, which involves uncertainty. A decade earlier my friends would have been content with being pointed to a useful reference book. Not so anymore! The work in almost every domain is done through computers and naturally they do have their data available in spreadsheets, databases, and sometimes in plain text format. The request for an appropriate statistical model is invariantly followed by a one word question software? My answer to them has always been a single letter reply R! Why? It is really a very simple decision and it has been my companion over the last seven years. In this book, this experience has been converted into detailed chapters and a cleaner breakup of model building in R.